Recently, the internet has achieved a remarkable development. The internet can deal with two-dimensional image and sound signals using Hyper Text Makeup Language (HTML) and, therefore, it can be used as a communication medium for image information, sound information, or other multimedia information.
The HTML, however, cannot deal with three-dimensional CG images although treating two-dimensional images, proposed as an alternative to the HTML is Virtual Reality Modeling Language (VRML) which enables three-dimensional image processing.
Nevertheless, VRML is hard to express three-dimensional CG images representing motions of human being. For expressing such images, it is necessary to treat the human being as a rigid body multi-joint object and to describe each joint's position or the like as time series data. This process, however, produces an enormous quantity of data.
Such problem about data amount can be solved by decomposing multi-dimensional time series data including joint positions into one-dimensional time series data and then performing compression to each one-dimensional time series data.
As such compression method for one-dimensional time series data, generally used is compression using orthogonal transform such as discrete Fourier transform and discrete cosine transformation. The orthogonal transform is described in several reference literatures including "Sound" by Kazuo Nakata, ed by Japan Acoustic Society, Colona, 1977. Time series data of joint angles of a rigid body multi-joint object is described in Unuma, M, et al., "Fourier Principles for Emotion-based Human Figure Animation", SIGGRAPH95 Proceedings, 1955, pp 91-95.
In the information compression method using orthogonal transform, given one-dimensional time series data is transformed into discrete Fourier series expansion expression and discrete cosine series expansion expression, using the data obtained at each time, and high-frequency component of the transformed series expansion expression, are eliminated to compress the information amount.
For the information compressed using such orthogonal transform, information decompression is performed by applying inverse Fourier transform and inverse cosine transformation. This produces, as decompressed data, not only data of the same number as the original data but data of arbitrary number which is different from the original data in number. However, the decompression of data is performed based on trigonometric function, and this may affect the decompressed data's properties.